Artificial intelligence certification of factsheets using blockchain

ABSTRACT

A method for blockchain certification of artificial intelligence factsheets that includes receiving by a computing device, an artificial intelligence model. The computing device generates an artificial intelligence factsheet based upon logic of the artificial intelligence model. The computing device generates a blockchain link for a blockchain. The blockchain link certifies the artificial intelligence factsheet. The computing device transmits the blockchain link certifying the artificial intelligence factsheet to other computing devices.

BACKGROUND

The field of embodiments of the present invention relate to artificialintelligence (AI) certification of factsheets using blockchain.

Valuation of artificial intelligence (AI) models in AI marketplacestoday is largely determined by uncertified factsheets by suppliers. Oneproblem with uncertified factsheets, however, is leaving customers todeal with the risk of later finding data or AI models to be invalid orunder-performing. Additionally, the information asymmetry may lead to aso-called market for ‘lemons’ and could eventually lead to marketcollapse.

SUMMARY

Embodiments relate to artificial intelligence (AI) certification offactsheets using blockchain. One embodiment provides a method forblockchain certification of AI factsheets that includes receiving by acomputing device, an AI model. The computing device generates an AIfactsheet based upon logic of the artificial intelligence model. Thecomputing device generates a blockchain link for a blockchain. Theblockchain link certifies the AI factsheet. The computing devicetransmits the blockchain link certifying the AI factsheet to othercomputing devices. These features contribute to the advantage ofcertification of AI factsheets so that a risk of finding data or AImodels to be invalid or underperforming later on is mitigated. Thefeatures further contribute to the advantage obviating the necessity fora centralized entity that provide AI factsheet certification.

One or more of the following features may be included. In someembodiments, the blockchain link provides an attestation certificatethat certifies AI resources.

In some embodiments, the AI resources are selected from the groupconsisting of an AI training resource, an AI testing resource, anddatasets with an associated factsheet and an AI validator

In some embodiments, the AI validator verifies metrics of the AI model.

In some embodiments, the certificate of attestation is applied forwithin a centralized marketplace for AI resources or a plurality ofdistributed marketplaces for AI resources.

In some embodiments, the blockchain is employed as a moderator andcertifying authority for AI resource requests for the centralizedmarketplace.

In some embodiments, the blockchain is employed for an AI repository forAI factsheets in each marketplace for the plurality of distributedmarketplaces.

These and other features, aspects and advantages of the presentembodiments will become understood with reference to the followingdescription, appended claims and accompanying figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a cloud computing environment, according to anembodiment;

FIG. 2 depicts a set of abstraction model layers, according to anembodiment;

FIG. 3 is a network architecture of a system for artificial intelligence(AI) certification of factsheets using blockchain processing, accordingto an embodiment;

FIG. 4 shows a representative hardware environment that may beassociated with the servers and/or clients of FIG. 1, according to anembodiment;

FIG. 5 is a block diagram illustrating a distributed system for AIcertification of factsheets using blockchain processing, according toone embodiment;

FIG. 6 is a block diagram illustrating a flow for certification of AIfactsheets processing, according to one embodiment;

FIG. 7A is an example listing for an AI resource factsheet, according toone embodiment;

FIG. 7B is an example listing for an AIValidator, according to oneembodiment;

FIG. 8A is an example flow diagram for issuing a certificate using asmart contract, according to one embodiment;

FIG. 8B is an example issued certificate using a smart contract,according to one embodiment;

FIG. 9 is an example for tracking changes in a factsheet and anAIValidator using blockchain records, according to one embodiment;

FIG. 10 is an example flow for using a blockchain as a moderator andcertifying authority, according to one embodiment;

FIG. 11 is an example flow for using a blockchain for AI resources, foran AI factsheet repository and for certificates, according to oneembodiment; and

FIG. 12 illustrates a block diagram of a process for AI certification offactsheets using blockchain processing, according to one embodiment.

DETAILED DESCRIPTION

The descriptions of the various embodiments have been presented forpurposes of illustration, but are not intended to be exhaustive orlimited to the embodiments disclosed. Many modifications and variationswill be apparent to those of ordinary skill in the art without departingfrom the scope and spirit of the described embodiments. The terminologyused herein was chosen to best explain the principles of theembodiments, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

Embodiments relate to blockchain certification of artificialintelligence (AI) factsheets. One embodiment provides a method forblockchain certification of AI factsheets that includes receiving by acomputing device, an AI model. The computing device generates an AIfactsheet based upon logic of the artificial intelligence model. Thecomputing device generates a blockchain link for a blockchain. Theblockchain link certifies the AI factsheet. The computing devicetransmits the blockchain link certifying the AI factsheet to othercomputing devices. Other embodiments include a computer program productfor blockchain certification of artificial intelligence factsheets, andan apparatus including a memory for storing instructions and a processorconfigured to execute the instructions. The method may further includethat the blockchain link provides an attestation certificate thatcertifies AI resources. The method may additionally include that the AIresources are selected from the group consisting of an AI trainingresource, an AI testing resource, and datasets with an associatedfactsheet and an AI validator. The method may further include that theAI validator verifies metrics of the AI model. The method may stillfurther include that the certificate of attestation is applied forwithin a centralized marketplace for AI resources or a plurality ofdistributed marketplaces for AI resources. The method may yet furtherinclude that the blockchain is employed as a moderator and certifyingauthority for AI resource requests for the centralized marketplace. Themethod may further include that the blockchain is employed for an AIrepository for AI factsheets in each marketplace for the plurality ofdistributed marketplaces.

A blockchain includes a growing list of records referred to as blocks,which are linked using cryptography. Each block in a blockchain mayinclude a cryptographic hash of the previous block in the chain, a timestamp and transaction data (e.g., represented as a tree such as a Merkletree, etc.). The information for each block is related to a transactionand each block is linked to a prior block in the chain. The blocks maybe tables and transactions as the records. Each transaction referencesthe transaction output of the previous transaction. Conventionalblockchains are resistant to modification of the data, and may include adistributed ledger that can record transactions between two partiesefficiently and in a verifiable and permanent way. When used as adistributed ledger, a blockchain is typically managed by a peer-to-peernetwork that follow a protocol for inter-node communication andvalidation of new blocks. Once recorded, the data in any block cannot bechanged retroactively without alteration of all subsequent blocks, whichmay require consensus of the network majority. A blockchain may beconsidered as a distributed transaction database among multiplecomputing devices. The computing devices can each have copies of theblockchain as new blocks are generated. Therefore, for conventionalblockchains no centralized or official copy of the blockchain exists andno computing device is trusted more than any other computing device.

AI resources herein refers to AI assets, such as an factsheets, AImodel(s), data, preprocessing programs, validation programs, evaluationprograms, and training and testing programs. AI models may include atrained machine learning model (e.g., models, such as a neural network(NN), a convolutional NN (CNN), a deep NN (DNN), a recurrent NN (RNN), aLong short-term memory (LSTM) based NN, gate recurrent unit (GRU) basedRNN, tree-based CNN, self-attention network (e.g., an NN that utilizesthe attention mechanism as the basic building block; self-attentionnetworks have been shown to be effective for sequence modeling tasks,while having no recurrence or convolutions), BiLSTM (bi-directionalLSTM), etc.).

An AI marketplace herein is interchangeably referred to as an AIplatform, which is a platform where sellers can list their AI resourcesand buyers can find and purchase AI resources. An AI marketplace mayoptionally provide a run time environment for executing training andtesting models.

An AI pipeline is referred to herein as a sequence of workflows in theAI development process. An AI pipeline also refers to the data andassociated programs for producing an AI model.

An AI factsheet is herein referred to as a certificate containing allthe AI resource details, quality metrics and other meta-information.

A supplier is herein referred to as an organization or individual thatowns AI resources and desires to sell them.

A buyer is herein referred to as an organization or individual willingto buy AI resources from the marketplace.

It is understood in advance that although this disclosure includes adetailed description of cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present embodiments are capable of being implementedin conjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines (VMs), and services)that can be rapidly provisioned and released with minimal managementeffort or interaction with a provider of the service. This cloud modelmay include at least five characteristics, at least three servicemodels, and at least four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded and automatically, without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneous,thin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or data center).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned and, in some cases, automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active consumer accounts). Resource usage canbe monitored, controlled, and reported, thereby providing transparencyfor both the provider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isthe ability to use the provider's applications running on a cloudinfrastructure. The applications are accessible from various clientdevices through a thin client interface, such as a web browser (e.g.,web-based email). The consumer does not manage or control the underlyingcloud infrastructure including network, servers, operating systems,storage, or even individual application capabilities, with the possibleexception of limited consumer-specific application configurationsettings.

Platform as a Service (PaaS): the capability provided to the consumer isthe ability to deploy onto the cloud infrastructure consumer-created oracquired applications created using programming languages and toolssupported by the provider. The consumer does not manage or control theunderlying cloud infrastructure including networks, servers, operatingsystems, or storage, but has control over the deployed applications andpossibly application-hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is the ability to provision processing, storage, networks, andother fundamental computing resources where the consumer is able todeploy and run arbitrary software, which can include operating systemsand applications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting for loadbalancing between clouds).

A cloud computing environment is a service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, an illustrative cloud computing environment 50is depicted. As shown, cloud computing environment 50 comprises one ormore cloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as private, community,public, or hybrid clouds as described hereinabove, or a combinationthereof. This allows the cloud computing environment 50 to offerinfrastructure, platforms, and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 1 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 2, a set of functional abstraction layers providedby the cloud computing environment 50 (FIG. 1) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 2 are intended to be illustrative only and embodiments are notlimited thereto. As depicted, the following layers and correspondingfunctions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, a management layer 80 may provide the functionsdescribed below. Resource provisioning 81 provides dynamic procurementof computing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and blockchain certification of AI factsheetsprocessing 96 (see, e.g., system 500, FIG. 5, flow 600, FIG. 6, process1200, FIG. 12). As mentioned above, all of the foregoing examplesdescribed with respect to FIG. 2 are illustrative only, and theembodiments are not limited to these examples.

It is reiterated that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather, theembodiments may be implemented with any type of clustered computingenvironment now known or later developed.

FIG. 3 is a network architecture of a system 300 for blockchaincertification of AI factsheets processing, according to an embodiment.As shown in FIG. 3, a plurality of remote networks 302 are provided,including a first remote network 304 and a second remote network 306. Agateway 301 may be coupled between the remote networks 302 and aproximate network 308. In the context of the present networkarchitecture 300, the networks 304, 306 may each take any formincluding, but not limited to, a LAN, a WAN, such as the Internet,public switched telephone network (PSTN), internal telephone network,etc.

In use, the gateway 301 serves as an entrance point from the remotenetworks 302 to the proximate network 308. As such, the gateway 301 mayfunction as a router, which is capable of directing a given packet ofdata that arrives at the gateway 301, and a switch, which furnishes theactual path in and out of the gateway 301 for a given packet.

Further included is at least one data server 314 coupled to theproximate network 308, which is accessible from the remote networks 302via the gateway 301. It should be noted that the data server(s) 314 mayinclude any type of computing device/groupware. Coupled to each dataserver 314 is a plurality of user devices 316. Such user devices 316 mayinclude a desktop computer, laptop computer, handheld computer, printer,and/or any other type of logic-containing device. It should be notedthat a user device 316 may also be directly coupled to any of thenetworks in some embodiments.

A peripheral 320 or series of peripherals 320, e.g., facsimile machines,printers, scanners, hard disk drives, networked and/or local storageunits or systems, etc., may be coupled to one or more of the networks304, 306, 308. It should be noted that databases and/or additionalcomponents may be utilized with, or integrated into, any type of networkelement coupled to the networks 304, 306, 308. In the context of thepresent description, a network element may refer to any component of anetwork.

According to some approaches, methods and systems described herein maybe implemented with and/or on virtual systems and/or systems, whichemulate one or more other systems, such as a UNIX® system that emulatesan IBM® z/OS environment, a UNIX® system that virtually hosts aMICROSOFT® WINDOWS® environment, a MICROSOFT® WINDOWS® system thatemulates an IBM® z/OS environment, etc. This virtualization and/oremulation may be implemented through the use of VMWARE software in someembodiments.

FIG. 4 shows a representative hardware system 400 environment associatedwith a user device 316 and/or server 314 of FIG. 3, in accordance withone embodiment. In one example, a hardware configuration includes aworkstation having a central processing unit 410, such as amicroprocessor, and a number of other units interconnected via a systembus 412. The workstation shown in FIG. 4 may include a Random AccessMemory (RAM) 414, Read Only Memory (ROM) 416, an I/O adapter 418 forconnecting peripheral devices, such as disk storage units 420 to the bus412, a user interface adapter 422 for connecting a keyboard 424, a mouse426, a speaker 428, a microphone 432, and/or other user interfacedevices, such as a touch screen, a digital camera (not shown), etc., tothe bus 412, communication adapter 434 for connecting the workstation toa communication network 435 (e.g., a data processing network) and adisplay adapter 436 for connecting the bus 412 to a display device 438.

In one example, the workstation may have resident thereon an operatingsystem, such as the MICROSOFT® WINDOWS® Operating System (OS), a MACOS®, a UNIX® OS, etc. In one embodiment, the system 400 employs a POSIX®based file system. It will be appreciated that other examples may alsobe implemented on platforms and operating systems other than thosementioned. Such other examples may include operating systems writtenusing JAVA®, XML, C, and/or C++ language, or other programminglanguages, along with an object oriented programming methodology. Objectoriented programming (OOP), which has become increasingly used todevelop complex applications, may also be used.

FIG. 5 is a block diagram illustrating a distributed system 500 forblockchain certification of AI factsheets processing, according to oneembodiment. In one embodiment, the system 500 includes client devices510 (e.g., mobile devices, smart devices, computing systems, etc.), acloud or resource sharing environment 520 (e.g., a public cloudcomputing environment, a private cloud computing environment, a datacenter, etc.), and servers 530. In one embodiment, the client devices510 are provided with cloud services from the servers 530 through thecloud or resource sharing environment 520.

Conventional certification of AI factsheets include using a centralizedentity that provides certificates. Some embodiments solve the necessityof deploying trust-able intermediaries. Blockchain systems implicitlyelicit trust. This is in contrast to centralized certifying authoritiesemployed in other technological areas where trust is not inherent. Indecentralized systems, trust is built by multiple levels of third partyintermediaries providing different levels of complexity.

Conventional AI pipelines have used blockchain support for variedpurposes. However, these conventional systems do not address the problemof providing a mechanism for the sellers to have their factsheetsattested. One embodiment solves the factsheet attestation problem usinga blockchain based mechanism that provides certificates for thefactsheet claims after verifying them. Using these certificates, sellerscan advertise their assets and buyers can trustfully buy them. In oneembodiment, a certificate of attestation is employed that validatesfactsheets claims made by the supplier using a blockchain as acertifying authority. The blockchain validates the claims in thefactsheet by the suppliers and provides time bound contracts. The AImodel/data suppliers can use this certificate to advertise theirmodel/data to prospective buyers. The certificate enables buyers to makeinformed decisions about the model and therefore guarantee theirpurchases are true to their claims.

FIG. 6 is a block diagram illustrating a flow 600 for certification ofAI factsheets processing, according to one embodiment. In oneembodiment, the flow 600 is performed by a processor or processingsystem (e.g., a computing device from computing node 10, FIG. 1,hardware and software layer 60, FIG. 2, processing system 300, FIG. 3,system 400, FIG. 4, system 500, FIG. 5, etc.). In one embodiment, anorganization 1 601 (e.g., a seller) has original data 605. Theorganization 1 601 trains at least one AI model using a training program610 to model metrics 615 of, for example, a metric of 80% accuracy. Atbullet 1 621, the organization 1 601 invokes transactions to write anAIValidator 616 (see, e.g., FIG. 7B) in the blockchain 617. TheAIValidator 616 asset represents the AI pipeline validation program. Atbullet 2 622, the flow 600 provides that the blockchain 617 records theinformation about the AIValidator 616. At bullet 3 623, the flow 600invokes a smart contract 635 (see FIG. 8A, flow 800) to verify andattest the model metrics 615. In bullet 4 624, the blockchain peers 630(peer nodes in the blockchain network) downloads the original data 605and the AI model and executes the validation and verification programs.At bullet 5 625 the blockchain 617 creates the attestation certificate640 (e.g., an electronic attestation certificate (Ecer)) and records orstores the attestation certificate 640 to a blockchain ledger 650. Atbullet 6 626 the blockchain 617 returns (e.g., via an electroniccommunication, etc.) the attestation certificate 640 to organization 1601. At bullet 7 627, the organization 1 601 shares the AI model, theoriginal data 605, model metrics 615 and the attestation certificate 640with organization 2 602.

In one embodiment, an AI marketplace may be a centralized AI marketplaceor a distributed AI marketplace. For the centralized AI marketplace, allof the AI resources (see, e.g., AI resource (factsheet) 710, FIG. 7A)and users of them are available in a single development platform. In oneembodiment, AI resources are available in a single logical store. Forthe distributed AI marketplace, the users and AI resources are notmanaged in a single store. Organizations can manage their own users andAI resources but can share their AI resources across organizations.

In one example embodiment, for a centralized marketplace using anattestation certificate 640, the process begins with an organizationapplying for the attestation certificate 640 to the blockchain 617. Asupplier can request certification for either or all of the following:an AI training resource on model generation on specific datasets, an AItesting resource where the data is applied to a model and output ischecked for performance metrics, and datasets along with its factsheetand AIValidator 616 for verifying the model metrics (e.g., model metric615), where each of these are associated with the AI resource that needsto be validated. AI resources such as data, model, factsheet andvalidation programs should be provided as input to the validationinterface. A blockchain 617 provides the following advantages incertification systems as compared to a centralized verification system.Firstly, a blockchain 617 facilitates participation of the organizationsthat share the assets directly in providing the certification, whereas asupplier organization in centralized verification systems has to rely ona third party central entity to provide the attestation certificates.One or more embodiments enhance the trust of the attestationcertificates generated using a blockchain 617. Secondly, eachparticipating entity (suppliers and requesting entity of AI resources)can set policies for issuing attestation certificates.

In one embodiment, the organization peers in the flow 600 providecomputing and storage resources to support the certification process. Itis in addition to the AI resources required for supporting the inherentblockchain operations, such as hash computations, storing of immutablerecords, etc. In one embodiment, the organization peers can beincentivized through separate incentive mechanisms to participate andcontribute to computing and storage resources. In one embodiment, theincentives may be proportional to the amount of AI resources theycontribute.

FIG. 7A is an example listing for an AI resource (factsheet) 710,according to one embodiment. The example listing of the AI resource(factsheet) 710 includes information such as user owner information,metadata, hash value, hash method and store reference. The AI resource(factsheet) 710 provides all information related to AI resources (e.g.,information related to data and an AI model).

FIG. 7B is an example listing for an AIValidator 616, according to oneembodiment. The AIValidator 616 contains the algorithm/function tovalidate the details with respect to the AI resources.

FIG. 8A is an example flow 800 diagram for issuing an attestationcertificate (e.g., attestation certificate 640, FIG. 6) using a smartcontract (e.g., smart contract 635, FIG. 6), according to oneembodiment. In one embodiment, in block 805 flow 800 identifies anAIValidator (e.g., AIValidator 616, FIGS. 6 and 7B). The AI Validator isan input provided by the user along with the AI resources. In block 810flow 800 identifies the AI resource (data) (e.g., AI resource 710, FIG.7A) supplied by a user (AI resources data, metadata and AI model aresupplied by the user). In block 815, flow 800 downloads the identifiedAI resource for processing. In block 820, flow 800 computes a hash(e.g., using a cryptographic function, etc.) on the AI resource. Inblock 825, the flow 800 determines is the hash is computed the same as aresource object (e.g., using conventional decryption techniques). If theresult of block 825 is no (i.e., the hash is not computed the same as aresource object), flow 800 proceeds to block 826 where the certificateapplication is rejected. Otherwise, flow 800 proceeds to block 830 whereit is determined if all resources are covered (or not). If it isdetermined that all resources are not covered in block 830, flow 800proceeds to block 810. Otherwise, flow 800 proceeds to block 835 wherethe association workflow is executed (the association workflow includesthe associated programs for validating the details of the AI resources.

In one embodiment, the flow 800 proceeds next to block 840 where it isdetermined whether the output (i.e., metrics details associated withdifferent AI resources generated from the associated programs) fromblock 835 has a same hash as in the identified AIValidator. If theoutput from block 835 does not have the same hash as the AIValidator,flow 800 proceeds to block 851 where the certificate application isrejected (i.e., not issued). Otherwise, flow 800 proceeds to block 845where the metrics (e.g., model metrics 615, FIG. 6) are verified (usingthe smart contract (e.g., smart contract 635, FIG. 6). In block 850, theflow 800 determines if the metrics for the model are the same as in theAIValidator. If it is determined that the metrics are the same as in theAIValidator, flow 800 proceeds to block 852 where the attestationcertificate (e.g., attestation certificate 640, FIG. 6) is issued.Otherwise, flow 800 proceeds to block 851 where the certificateapplication is rejected (i.e., not issued).

FIG. 8B is an example issued attestation certificate 640 using a smartcontract (e.g., smart contract 635, FIG. 6), according to oneembodiment. In one embodiment, the attestation certificate 640 isencrypted using the public key of a seller. The attestation certificate640 may include information, such as the AIValidator used, whether theattestation certificate 640 is validated or not, whether the attestationcertificate 640 is active or not, the time of creation, time ofexpiration, etc.

FIG. 9 is an example flow diagram 900 for tracking changes in afactsheet (e.g., factsheet 710, FIG. 7A) and an AIValidator (e.g.,AIValidator 616, FIGS. 6, 7B) using blockchain records 920, according toone embodiment. In the flow diagram 900, the initial information is forData1 911 and blockchain record 921. The flow diagram 900 starts withthe oldest 940 records to the latest 945 records. When a user action 910modifies the Data1 911 at modify data 930, the result is Data1 912 and ablockchain record 922, which will have a modified or new hash value(HashValue=002). The blockchain record 922 is chained or added to theblockchain record 921. Then, when another user action 910 modifies theData1 912 at modify data 935, the result is Data1 913 and a blockchainrecord 923, which will have a modified or new hash value(HashValue=003). The blockchain record 923 is chained or added to theprior blockchain record 922. This sequence continues as long as the datais modified/changed.

FIG. 10 is an example flow 1000 for using a blockchain as a moderatorand certifying authority, according to one embodiment. Flow 1000considers where different organizations have their own AI platform todevelop models, but use an offline channel to share the AI resources andfactsheets. In one embodiment, the AI platforms do not share acentralized repository for storing the AI resources and factsheets. Forthis decentralized AI platform scenario, two approaches for certifyingthe factsheets may be employed depending on whether the supplier iswilling to use the blockchain as a repository of AI resources orotherwise. In the first approach (flow 1000), the blockchain is employedas a ‘moderator’ for any requests. In the second approach (flow 1100,FIG. 11), the blockchain peers 630 act as a repository of AI factsheetsand provides a certified factsheet.

In one embodiment, the flow 1000 is performed by a processor orprocessing system (e.g., a computing device from computing node 10, FIG.1, hardware and software layer 60, FIG. 2, processing system 300, FIG.3, system 400, FIG. 4, system 500, FIG. 5, etc.). In one embodiment, anorganization 1 601 (e.g., a seller) has original data 605. Theorganization 1 601 trains at least one AI model using a training program610 to model metrics 615 of, for example, a metric of 80% accuracy. Atbullet 1 1010, the organization 1 601 transfers the association betweendata, program and model produces the attestation certificate 640 and theAIValidator 616 to the organization 2 602. At bullet 2 1020, the flow1000 provides that the organization 2 602 submits the attestationcertificate 640 to the blockchain peers 630. At bullet 3 1030, the flow1000 provides that the blockchain AI resources are downloaded and theattestation certificate 640 is verified using a verification program. Atbullet 4 1040, the flow 1000 provides that the blockchain peers 630create another attestation certificate 640 and records it in theblockchain ledger (e.g., blockchain ledger 650, FIG. 6). At bullet 51050, the flow 1000 provides that the blockchain peers 630 stores the AIresources in its remote data store 1080. At bullet 6 1060, the flow 1000provides that the blockchain peers 630 prepares a new AIValidator objectusing the smart contract 635. At bullet 7 1070, the flow 1000 providesthat the blockchain peers 630 returns the new AIValidator object and thenew attestation certificate 640 to the organization 2 602.

FIG. 11 is an example flow 1100 for using a blockchain (blockchain peers630) for AI resources (e.g., factsheets, etc.) that is used for an AIfactsheet repository and for certificates (e.g., attestation certificate640), according to one embodiment. In flow 1100 the blockchain peers 630act as a repository of AI factsheets and provides a certified factsheet.In one embodiment, the flow 1100 is performed by a processor orprocessing system (e.g., a computing device from computing node 10, FIG.1, hardware and software layer 60, FIG. 2, processing system 300, FIG.3, system 400, FIG. 4, system 500, FIG. 5, etc.). In one embodiment, anorganization 1 601 (e.g., a seller) has original data 605. Theorganization 1 601 trains at least one AI model using a training program610 to model metrics 615 of, for example, a metric of 80% accuracy. Atbullet 1 1110, the organization 1 601 produces an AIValidator object forthe blockchain peers 630 using the AIValidator 616. At bullet 3 1120,the flow 1100 provides that the blockchain AI resources are downloadedand the attestation certificate 640 is verified using a verificationprogram. At bullet 4 1130, the flow 1100 provides that the blockchainpeers 630 creates another attestation certificate 640 and records it inthe blockchain ledger (e.g., blockchain ledger 650, FIG. 6). At bullet 51140, the flow 1100 provides that the blockchain peers 630 stores the AIresources in its own data store. At bullet 6 1150, the flow 1100provides that the blockchain peers 630 prepares a new AIValidator objectusing the smart contract 635. At bullet 7 1160, the flow 1100 providesthat the blockchain peers 630 returns the new AIValidator object and thenew attestation certificate 640 to the organization 1 601. At bullet 81170, the flow 1100 provides that the organization 1 601 shares the newattestation certificate 640 with the organization 2 602.

FIG. 12 illustrates a block diagram of a process 1200 for AIcertification of factsheets using blockchain processing, according toone embodiment. In one embodiment, in block 1210 process 1200 utilizes acomputing device (from computing node 10, FIG. 1, hardware and softwarelayer 60, FIG. 2, processing system 300, FIG. 3, system 400, FIG. 4,system 500, FIG. 5, etc.) for receiving an AI model. In block 1220,process 1200 further provides for generating, by the computing device,an AI factsheet (e.g., AI factsheet 710) based upon logic of the AImodel. In block 1230, process 1200 further provides for generating, bythe computing device, a blockchain link for a blockchain (e.g.,blockchain peers 630, FIGS. 6, 10 and 11) the blockchain link forcertifying the AI factsheet. In block 1240, process 1200 furtherprovides for transmitting, by the computing device, the blockchain linkcertifying the AI factsheet to multiple other computing devices.

In one embodiment, process 1200 may further include the feature that theblockchain link provides an attestation certificate (e.g., including anattestation certificate 640, FIGS. 6, 10 and 11) that certifies AIresources. The method may additionally include that the AI resources areselected from the group consisting of an AI training resource, an AItesting resource, and datasets with an associated factsheet and an AIvalidator (e.g., AIValidator 616, FIGS. 6, 7B, 10 and 11). The methodmay further include that the AI validator verifies metrics (e.g.,metrics 615, FIGS. 6, 10 and 11) of the AI model. The method may stillfurther include that the certificate of attestation is applied forwithin a centralized marketplace for AI resources or multipledistributed marketplaces for AI resources. The method may yet furtherinclude that the blockchain is employed as a moderator and certifyingauthority for AI resource requests for the centralized marketplace. Themethod may further include that the blockchain is employed for an AIrepository for AI factsheets in each marketplace for the multipledistributed marketplaces.

In some embodiments, the features described above contribute to theadvantage of overcoming the problem with uncertified factsheets, whichcan leave customers to deal with the risk of later finding data or AImodels to be invalid or under-performing, or that of informationasymmetry. Some embodiments provide the advantage of certification of AIfactsheets so that a risk of finding data or AI models to be invalid orunderperforming later on is mitigated. The features further contributeto the advantage obviating the necessity for a centralized entity thatprovide AI factsheet certification. Other advantages are that ablockchain can verify the claims of factsheets by executing a smartcontract (e.g., smart contract 635, FIG. 6) that encapsulates the logicfor ascertaining the AI factsheet claims. Using the attestationcertificates (e.g., e.g., attestation certificate 640, FIGS. 6, 10 and11), customers can trust the factsheet claims made by the suppliers andprocure these assets with guarantees. Further advantages include amethod by which sellers can publish their AI assets along with the AIfactsheet and to verify the AI factsheet to the blockchain. Someembodiments provide the features of methods to incentivizes blockchainnodes to offer certificate services to AI marketplaces and associated AIfactsheets. Attestation certificates issued by the blockchain aremanaged throughout their lifecycle.

One or more embodiments may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present embodiments.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe embodiments may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present embodiments.

Aspects of the embodiments are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products. It will be understood thateach block of the flowchart illustrations and/or block diagrams, andcombinations of blocks in the flowchart illustrations and/or blockdiagrams, can be implemented by computer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a computer, or other programmable data processing apparatusto produce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks. These computerreadable program instructions may also be stored in a computer readablestorage medium that can direct a computer, a programmable dataprocessing apparatus, and/or other devices to function in a particularmanner, such that the computer readable storage medium havinginstructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments. In this regard, each block in the flowchart or blockdiagrams may represent a module, segment, or portion of instructions,which comprises one or more executable instructions for implementing thespecified logical function(s). In some alternative implementations, thefunctions noted in the blocks may occur out of the order noted in theFigures. For example, two blocks shown in succession may, in fact, beaccomplished as one step, executed concurrently, substantiallyconcurrently, in a partially or wholly temporally overlapping manner, orthe blocks may sometimes be executed in the reverse order, dependingupon the functionality involved. It will also be noted that each blockof the block diagrams and/or flowchart illustration, and combinations ofblocks in the block diagrams and/or flowchart illustration, can beimplemented by special purpose hardware-based systems that perform thespecified functions or acts or carry out combinations of special purposehardware and computer instructions.

References in the claims to an element in the singular is not intendedto mean “one and only” unless explicitly so stated, but rather “one ormore.” All structural and functional equivalents to the elements of theabove-described exemplary embodiment that are currently known or latercome to be known to those of ordinary skill in the art are intended tobe encompassed by the present claims. No claim element herein is to beconstrued under the provisions of 35 U.S.C. section 112, sixthparagraph, unless the element is expressly recited using the phrase“means for” or “step for.”

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the embodiments.As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present embodiments has been presented for purposesof illustration and description, but is not intended to be exhaustive orlimited to the embodiments in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the embodiments. Theembodiment was chosen and described in order to best explain theprinciples of the embodiments and the practical application, and toenable others of ordinary skill in the art to understand the embodimentsfor various embodiments with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. A method for blockchain certification ofartificial intelligence factsheets, the method comprising: receiving, bya computing device, an artificial intelligence model; generating, by thecomputing device, an artificial intelligence factsheet based upon logicof the artificial intelligence model; generating, by the computingdevice, a blockchain link for a blockchain, the blockchain link forcertifying the artificial intelligence factsheet; and transmitting, bythe computing device, the blockchain link certifying the artificialintelligence factsheet to a plurality of other computing devices, wherein the blockchain link provides an attestation certificate thatcertifies artificial intelligence resources, and the artificialintelligence resource comprises datasets with an associated factsheet.2. The method of claim 1, wherein the artificial intelligence resourcesare further selected from the group consisting of an artificialintelligence training resource, an artificial intelligence testingresource, artificial intelligence models, artificial intelligencepreprocessing programs, and an artificial intelligence validator.
 3. Themethod of claim 2, wherein the artificial intelligence validatorverifies metrics of the artificial intelligence model.
 4. The method ofclaim 1, wherein the certificate of attestation is applied for within acentralized marketplace for artificial intelligence resources or aplurality of distributed marketplaces for artificial intelligenceresources.
 5. The method of claim 4, wherein the blockchain is employedas a moderator and certifying authority for artificial intelligenceresource requests for the centralized marketplace.
 6. The method ofclaim 4, wherein the blockchain is employed for an artificialintelligence repository for artificial intelligence factsheets in eachmarketplace for the plurality of distributed marketplaces.
 7. A computerprogram product for blockchain certification of artificial intelligencefactsheets, the computer program product comprising a computer readablestorage medium having program instructions embodied therewith, theprogram instructions executable by a processor to cause the processorto: receive, by the processor, an artificial intelligence model;generate, by the processor, an artificial intelligence factsheet basedupon logic of the artificial intelligence model; generate, by theprocessor, a blockchain link for a blockchain, the blockchain link forcertifying the artificial intelligence factsheet; and transmit, by theprocessor, the blockchain link certifying the artificial intelligencefactsheet to a plurality of other computing devices,  wherein theblockchain link provides an attestation certificate that certifiesartificial intelligence resources, and the artificial intelligenceresource comprises datasets with an associated factsheet.
 8. Thecomputer program product of claim 7, wherein the artificial intelligenceresources are further selected from the group consisting of anartificial intelligence training resource, an artificial intelligencetesting resource, artificial intelligence models, artificialintelligence preprocessing programs, and an artificial intelligencevalidator.
 9. The computer program product of claim 8, wherein theartificial intelligence validator verifies metrics of the artificialintelligence model.
 10. The computer program product of claim 7, whereinthe certificate of attestation is applied for within a centralizedmarketplace for artificial intelligence resources or a plurality ofdistributed marketplaces for artificial intelligence resources.
 11. Thecomputer program product of claim 10, wherein the blockchain is employedas a moderator and certifying authority for artificial intelligenceresource requests for the centralized marketplace.
 12. The computerprogram product of claim 10, wherein the blockchain is employed for anartificial intelligence repository for artificial intelligencefactsheets in each marketplace for the plurality of distributedmarketplaces.
 13. An apparatus comprising: a memory configured to storeinstructions; and a processor configured to execute the instructions to:receive an artificial intelligence model; generate an artificialintelligence factsheet based upon logic of the artificial intelligencemodel; generate a blockchain link for a blockchain, the blockchain linkfor certifying the artificial intelligence factsheet; and transmit theblockchain link certifying the artificial intelligence factsheet to aplurality of other computing devices, wherein the blockchain linkprovides an attestation certificate that certifies artificialintelligence resources, and the artificial intelligence resourcecomprises datasets with an associated factsheet.
 14. The apparatus ofclaim 13, wherein the artificial intelligence resources are selectedfrom the group consisting of an artificial intelligence trainingresource, an artificial intelligence testing resource, artificialintelligence models, artificial intelligence preprocessing programs, andartificial intelligence validator.
 15. The apparatus of claim 14,wherein the artificial intelligence validator verifies metrics of theartificial intelligence model, and the certificate of attestation isapplied for within a centralized marketplace for artificial intelligenceresources or a plurality of distributed marketplaces for artificialintelligence resources.
 16. The apparatus of claim 15, wherein theblockchain is employed as a moderator and certifying authority forartificial intelligence resource requests for the centralizedmarketplace.
 17. The apparatus of claim 15, wherein the blockchain isemployed for an artificial intelligence repository for artificialintelligence factsheets in each marketplace for the plurality ofdistributed marketplaces.